1. Field of the Invention
The present invention relates to a method and apparatus for performing pattern recognition on an input pattern.
More specifically, the present invention relates to a method and apparatus for performing pattern recognition on a pattern obtained from input speech information so as to produce a sentence corresponding to the input speech information.
Furthermore, the present invention relates to a method and apparatus for achieving accurate pattern recognition with a reduced amount of computing operations.
2. Description of the Related Art
It is known in the art in pattern recognition technique that reference patterns are prepared in advance and a reference pattern which shows the best matching with the input pattern is selected and employed as the recognition result. In general, an input pattern can be represented by a feature vector including a plurality of feature values. On the other hand, the reference patterns can be represented as feature vectors representative of recognition results or represented by a function of the feature vector.
When the reference patterns are given as feature vectors representative of recognition results, the degree of matching between an input pattern and a reference pattern is represented by the distance between the feature vector associated with the input pattern and the feature vector associated with the reference pattern. On the other hand, if the reference patterns are given by a function of the feature vector, the degree of the matching between an input pattern and a reference pattern is represented by a value of the reference function of a given feature vector associated with an input pattern.
In many cases, the reference pattern function is given in a form of a multidimensional probability density function. If a given feature vector and the probability density function for an ith reference pattern are denoted by x and P.sup.i (.multidot.), respectively, then the degree of matching Y.sup.i between the input pattern and the ith reference pattern is given by: EQU Y.sup.i =P.sup.i (x) (1)
A function such as a Gaussian distribution function is employed as the probability density function. In some cases, a mixed density function such as the weighted sum of a plurality of probability density functions is also employed. When a mixed density function is employed, the degree of matching is represented by: ##EQU1## where P.sup.i.sub.m (.multidot.) is the mth probability density function associated with the ith reference pattern, and w.sup.i.sub.m is the weight of the mth probability density function associated with the ith reference pattern.
Furthermore, when there is no correlation among the dimensions elements of the feature vector given by equation (1), the following function may also be employed as the reference pattern function: ##EQU2## where x.sub.j is the feature value of the jth-dimension element of an input vector x, and P.sup.i.sub.j (.multidot.) is the probability density function corresponding to the jth-dimension element of the ith reference pattern.
In speech recognition, a hidden Markov model (HMM) is usually employed. In this case, the reference patterns correspond to individual HMM states, and the each HMM state represents the output probability corresponding to the input pattern.
In practical pattern recognition, P.sup.i (.multidot.) merely represents the degree of matching between an input pattern and a reference pattern, and thus P.sup.i (.multidot.) is not necessarily required to be a probability density function in a rigorous sense. P.sup.i (.multidot.) can be regarded as the distance between an input vector and a reference pattern vector. Furthermore, a usual function other than distance functions may also be employed as P.sup.i (.multidot.). Thus, in the following description, the term "reference pattern function" or "matching degree function" is used instead of the term "probability density function".
A problem in the conventional technique of pattern recognition described above is that equation (1) has to be calculated for all reference pattern functions. In particular, when there are a great number of reference patterns, it is required to perform a great amount of calculations to determine the degree of matching.